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What Tech Carriers, Forwarders, and Shippers Think Will Shape 2026 Freight Procurement

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What Tech Carriers, Forwarders, and Shippers Think Will Shape 2026 Freight Procurement

October 29, 2025

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The recent FreighTech 2025 conference once again brought together a mix of carriers, forwarders, BCOs, academics, and tech providers to hear the latest and share insights around, logistics technology and how it can benefit the freight industry.

Key Tech Trends for Global Freight in 2026

From AI to ocean freight innovation and tendering strategies for 2026, here are some of the key takeaways from this year’s event.

AI is already having an impact, especially in data processing and customer support, with expectations to manage 20% of human tasks within five years – though most organizations aren’t fully AI-ready yet.

Data quality challenges and few standards remain an obstacle for tech implementation, including for AI projects.

Ocean freight digitalization is progressing, with improved carrier APIs expected to trigger a digital transformation similar to what’s occurred in air cargo.

Strategic approaches to tendering are maturing, as technology tools for pricing visibility and rate discovery are helping companies move away from underutilized contracts on low-volume lanes toward a more balanced contract/spot strategy – as MIT research presented at the conference recommends.

Index-linked freight contracts are gaining traction as these flexible agreements are proving beneficial for all parties,often offering better costs, revenue, and reliability than traditional fixed contracts.

AI for Global Freight: Potential, Reality and Best Practices

Key Takeaways:

AI is already being used aggressively, mostly with data processing, automation and front-line customer support.

It’s still just getting started; leaders have high expectations to handle more tasks in coming years with human roles mostly evolving, rather than being eliminated, as a result.

Many are convinced that AI will have a net negative impact on sector employment.

Leaders don’t feel completely ready yet

AI was, as expected, a hot topic this year and top of mind for most in attendance. But discussion focussed on separating current practical AI applications for logistics from the hype, setting realistic horizons, and sharing lessons learned to date.

An audience poll showed expectations that within the next few years AI will handle a meaningful share of current logistics tasks currently done by humans – over half of leaders believe that at least 20% of current roles could be handled by AI in the next five years.

But there was also consensus that, along with some admitted reduction in headcount, human roles will evolve along with AI advances. Logistics professionals will leverage AI to enable teams to do more, and add more value for customers in new ways – just as many logistics tech introductions to date have enhanced instead of eliminated human roles.

Freight is complicated, however, and speakers agreed that AI can’t do it all, and not right away. At the same time, AI is already being applied in multiple ways across the freight landscape, especially for mundane and repetitive tasks. Some examples include using AI to:

Detect data anomalies or process unstructured data

Create content

Enable automation flow between systems

Power agents (and even voicebots) that handle routine customer inquiries or internal processes.

They still aren’t ready though.

That being said, only about a third in attendance consider their organizations AI-ready.

As such, best practices for AI investment, development, and introductions for logistics from those already at the forefront focussed on the following main recommendations:

Problem Mapping: Identify high-impact, high-frequency problems where AI is already likely to add value

Start Small: Begin with clear use cases and expand based on success

Focus: Build AI capabilities in areas where your company has deep domain expertise, and buy solutions for everything else

Experiment and share: Make AI tools available to teams for experimentation and facilitate knowledge sharing.

Data Quality – the Persistent Roadblock

Key Takeaways:

Lack of standards and inconsistent data remains a frustrating roadblock, including for AI

Focus on data that does work; scale from there

But even alongside the excitement surrounding AI, there was a familiar refrain that poor data quality – often from data received from partners in the supply chain, and attributable to the ongoing lack of freight data standards – continues to frustrate some logistics tech aspirations, including AI projects.

“Discovering something you can’t do right now is also important, and opens new opportunities to do that thing, and maybe more, in the future. In this case it showed the value of investing in quality data.” – Robert Khachatryan, CEO FreightRight

Robert Khachatryan of FreightRight shared a case study of the forwarder’s attempt to build an AI-driven predictive pricing system pilot with data scientists from USC. But the project had to be scrapped when they realized that much of their necessary historical freight rate data – where the inputs are complex and varied – was not clean enough to enable AI to succeed in the task.

The lesson learned was that investing in data quality now will enable successful tech, including AI, in the future.

“On the carrier side, we’ve established a shared understanding of what information we can easily exchange right now, and so we focus on that available data for digital solutions, to improve our efficiency and the customer experience.” – Helge Neumann-Lezius, Head of FCL, Hellmann

Helge Neumann-Lezius from Hellmann offered Hellman’s similar pragmatic approach to tech investment and roll outs: focus on building around the quality data you have now, while taking steps to improve data quality in other areas.

Ocean Innovation: Nearing a Digital Tipping Point

Key Takeaways:

Ocean liners are beginning to improve access to APIs

This will likely help fuel the same surge in connectivity that airline APIs have offered.

An example of this strategy is Hellman’s focus on more real-time data exchange with ocean carriers, leveraging improved API connections with carriers to enable real-time rate and tracking data.

So while ocean freight’s digital adoption has lagged air cargo’s – where API-enabled dynamic rates, market intelligence, and eBookings, including through third party platforms, are becoming more and more prevalent – several speakers suggested that ocean is approaching its tipping point.

The logistics supply chain is often only as digitalized as its least digital partner, so as ocean carrier connectivity improves the near term is likely to see a surge in digital ocean freight, including real-time rates, online bookings and TMS integrations.

Tendering in 2026: Finding the Right Balance

Key Takeaways:

Long term contracts are assumed to be the default solution for tendering but can be unreliable or underutilized

Spot freight – used strategically – can reduce costs and save time

Index-linked contracts and even hedging are gathering momentum after many years of discussion.

Looking ahead to 2026, several discussions on ocean freight tendering for the coming year revealed interesting recommendations for striking a contract vs. spot balance, explored the growing prevalence of index-linked contracts, and shared how tech is playing a larger role here as well.

Dr. Angi Acocella of MIT’s Center for Transportation and Logistics shared her recent research showing that shippers in both FTL and ocean rely on long term contracts for the big majority of their volumes, using spot to manage uncertainty – mostly for unexpected volumes, one-off shipments or lanes, or when contracted carriers are unavailable.

But the research also showed an 80/20 split: 80% of shipper volumes go on 20% of the contracted lanes, leaving many contracts for lower-volume lanes underutilized. Unused contracts not only cost shippers in the form of wasted time and resources on negotiations, but also often entail higher rates for shipments moved on these lanes – often at levels above spot costs for those shipments – and slightly higher contract rates on high volume lanes as well.

As such, she recommends examining lane volumes, contract rates and spot usage and costs from the previous year. Shippers are advised to focus contracts on the higher volume lanes, and rely more on spot for the long tail.

Research also shows the growing place for index-linked contracts in freight, and evidence that index-linked contracts benefit both carriers and shippers in the form of lower costs, increased revenue and better volume reliability than non-linked contracts or the spot market.

Multiple speakers noted the importance of trust for index-linked pilots – trust between the partners, in the rate data selected as the basis, and in the contract mechanism. As these grow, index-linked contract adoption is expected to grow as well.

Finally, speakers touched on the increased importance of technology to procurement. Tools that improve pricing/volume visibility, rate discovery, and the speed and efficiency of communication between carriers/LSPs/shippers already contribute to the ability to make better and strategic tendering decisions. Tech-enabled improvements in these areas are helping shippers and LSPs make the procurement process – for both tenders and spot shipments – less costly, faster, more efficient, and more reliable.
If you enjoyed this, you may also enjoy our recent virtual summit, which included discussions of digital freight transformation, spot/tender balances, and more. See it here.

Judah Levine

Head of Research, Freightos Group

Judah is an experienced market research manager, using data-driven analytics to deliver market-based insights. Judah produces the Freightos Group’s FBX Weekly Freight Update and other research on what’s happening in the industry from shipper behaviors to the latest in logistics technology and digitization.

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Supply Chain and Logistics News February 23rd- 26th 2026

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Supply Chain And Logistics News February 23rd 26th 2026

This week’s supply chain landscape is defined by a massive push to bridge the gap between having data and actually using it. From the high-stakes legal battle over billion-dollar tariffs to a radical AI-driven workforce restructuring at WiseTech Global, the industry is moving past simple visibility toward a period of high-consequence execution. Whether it is the Supreme Court’s intervention in trade policy or the operationalization of decision intelligence showcased at the 30th Annual ARC Forum, the recurring theme is clear: the next competitive advantage belongs to those who can synchronize their technology, their inventory, and their legal strategies in real time. In this edition, we break down the four critical shifts—architectural, legal, operational, and structural—shaping the final days of February 2026.

Your News for the Week:

The Technology Gap: Why Supply Chain Execution Still Isn’t Fully Connected Yet

Richard Stewart of Infios argues that the primary technology gap in modern supply chain execution is not a lack of ambition or budget, but rather an architectural failure. Most existing systems, such as WMS and TMS, are designed to optimize within their own silos, leaving a critical disconnect during real-time disruptions where manual workarounds and spreadsheets are still required to coordinate responses. Citing the Supply Chain Execution Readiness Report, Richard highlights that 69% of leaders struggle with data quality and integration, driving a shift in buying criteria toward interoperability and real-time visibility. Ultimately, Richard suggests that the next competitive advantage will belong to organizations that move beyond simple visibility toward “connected execution,” prioritizing modular architectures that synchronize decisions across the entire operational landscape rather than just reporting on them.

FedEx sues the US Government, seeking a full refund over Trump Tariffs

FedEx has officially filed a lawsuit against the US government, seeking a full refund for duties paid under the Trump administration’s recent tariff policies. The move follows a landmark 6-3 Supreme Court ruling that found the president overstepped his authority by using emergency powers to bypass Congress’s sole power to levy taxes. While the court’s decision stopped the specific enforcement mechanism, it left the status of the estimated $175 billion already collected in limbo. As the first major carrier to seek reimbursement, FedEx’s legal challenge could set a precedent that could affect the logistics industry and thousands of other importers currently navigating a volatile trade environment.

From Hidden Inventory to Returns Recovery: Exposing Operational Blind Spots

Hiu Wai Loh sheds light on the hidden inventory crisis and the costly returns black hole that plagues supply chains long after peak season ends. The research reveals that a staggering number of organizations suffer from fragmented data, leading to false stockouts and millions of dollars trapped in reverse logistics limbo. To overcome these operational blind spots, the author argues that companies must tear down silos and adopt a unified, real-time inventory model. By leveraging AI-driven smart disposition, businesses can efficiently route returns to their most profitable next destination, transforming a traditional cost center into a powerful engine for full-price recovery and year-round agility.

How Avantor and Aera Technology Are Operationalizing Decision Intelligence, Insights from ARC Advisory Group’s 30th Leadership Forum

Avantor and Aera Technology were present at the 30th Annual ARC Forum and presented on how they are operationalizing Decision Intelligence. They explore how modern supply chains are navigating the paradox of increasing global disruptions alongside record-breaking operational efficiency. By highlighting a case study from Avantor, the presentation demonstrated how Decision Intelligence (DI) can move beyond theoretical AI to automate thousands of routine daily decisions, such as stock rebalancing and purchase order prioritization. The key takeaway from the ARC Advisory Group’s 30th Leadership Forum is that companies should focus on “change-ready” solutions that solve immediate, high-impact problems rather than waiting for perfect data or fully autonomous systems.

WiseTech Global Cutting 30% of Workforce in AI restructure:

WiseTech Global, the developer of the CargoWise platform, has announced a major two-year restructuring plan that will involve cutting approximately 2,000 jobs, or 29% of its global workforce. This strategic pivot aims to integrate artificial intelligence deeper into both its internal operations and its customer-facing software, which currently handles a massive 75% of global customs transaction data. The layoffs are expected to hit the company’s U.S. cloud division, E2open, particularly hard, with some reports suggesting cuts of up to 50% there. This move comes at a turbulent time for the Australian tech giant, as it seeks to regain investor confidence following a 68% drop in share price since late 2024 amid leadership controversies and shifting market dynamics.

Song of the week:

The post Supply Chain and Logistics News February 23rd- 26th 2026 appeared first on Logistics Viewpoints.

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Burger King’s AI “Patty” Moves AI Into Frontline Execution

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Burger King’s Ai “patty” Moves Ai Into Frontline Execution

Burger King is piloting an AI assistant called “Patty” inside employee headsets as part of its broader BK Assistant platform. This is not a marketing chatbot. It is an operational system embedded into restaurant execution.

Patty supports crew members with preparation guidance, monitors equipment status, and analyzes customer interactions for defined service language such as “please” and “thank you.” Managers can query performance metrics tied to service quality in real time.

The architecture matters more than the novelty.

AI Inside the Operational Core

Patty is integrated with a cloud based point of sale system. That connection allows:

near real time inventory updates across channels
equipment downtime alerts
synchronized digital menu adjustments
structured service quality measurement

If a product goes out of stock or a machine fails, availability can be updated across kiosks, drive through boards, and digital systems within minutes.

This is AI operating inside the transaction layer, not sitting above it.

Earlier fast food AI experiments focused on automated drive through ordering. Burger King is more measured there. The more consequential shift is internal execution intelligence.

Efficiency, Visibility, and Risk

Across retail and logistics sectors, AI agents are being embedded directly into workflows to standardize performance and compress response times. The value comes from integration and coordination, not conversational capability.

At the same time, customer sentiment toward fully automated service remains mixed. Privacy, workforce implications, and over automation risk are active concerns. As AI begins monitoring tone and behavior, governance becomes part of the deployment decision.

Operational AI improves visibility. It also expands accountability.

Implications for Supply Chain and Operations Leaders

Three themes emerge:

Execution instrumentation – AI is now measuring soft metrics and converting them into structured operational data.
Closed loop response – When connected to POS and inventory systems, AI can both detect issues and trigger corrective updates.
Governance at scale – Embedding AI at the edge requires clear oversight, performance auditability, and workforce alignment.

Burger King plans to expand BK Assistant across U.S. restaurants by the end of 2026, with Patty currently piloting in several hundred locations.

This is not a fast food curiosity. It is a signal.

AI is moving from analytics to execution. From dashboards to headsets. From advisory tools to operational participants.

For supply chain leaders, the question is no longer whether AI will enter frontline operations. The question is how intentionally it will be architected and governed once it does.

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AI and Enterprise Software: Is the “SaaSpocalypse” Narrative Overstated?

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Ai And Enterprise Software: Is The “saaspocalypse” Narrative Overstated?

Capital is rotating. Growth has given way to value, and within technology the divergence is increasingly pronounced. While broad indices have stabilized, many software names have not. Since late 2025, software equities have materially underperformed other parts of the technology complex. Forward revenue growth across many mid-cap SaaS firms has slowed from prior expansion levels, net retention rates have edged down in several categories, and valuation multiples have compressed accordingly. Markets are repricing both growth durability and margin structure.

The prevailing explanation is straightforward. Generative AI lowers barriers to entry, reduces the cost of building applications, and compresses differentiation. If application logic becomes easier to produce, competitive intensity increases and pricing power weakens. The result is visible not only in equity valuations, but in moderated expansion rates and tighter forward guidance. There is substance behind that concern. But reducing enterprise software economics to code production misses where the structural leverage in these platforms actually resides.

The Core Bear Case

The bearish thesis rests on three related propositions: AI commoditizes application logic, accelerates competitive entry, and pressures margins. If enterprises can generate software dynamically, recurring subscription models face structural pressure. If workflows can be automated through agents, reliance on fixed applications may decline. If code becomes less scarce, incumbents may struggle to defend premium multiples.

The repricing in software reflects these risks. Multiples have compressed meaningfully, and growth expectations have moderated across several verticals. In certain categories, retention softness suggests substitution pressure is already emerging. These signals should not be dismissed as temporary volatility.

At the same time, equating software value solely with feature output or code generation is a simplification. Enterprise software durability rarely rests on feature sets alone.

What Enterprise Software Actually Represents

In supply chain environments, systems function as operational coordination layers rather than isolated applications. Transportation management systems, warehouse platforms, planning suites, and multi-enterprise visibility networks sit at the center of integrated transaction flows. They embed years of configuration, exception handling logic, compliance mappings, and cross-functional workflows. Over time, they accumulate operational data that informs sourcing, forecasting, transportation optimization, and execution decisions across the enterprise.

Replacing those systems is not equivalent to generating new code. It requires rebuilding institutional memory, re-establishing integration points, and re-validating compliance controls across internal and external stakeholders. The switching cost is not interface retraining; it is operational re-architecture.

In our research on AI system design in supply chains

AI in the Supply Chain-sp

, the recurring conclusion is that structural advantage stems from coordination, persistent context, and integration density. Model capability matters. Economic durability flows from how systems connect and govern activity across distributed networks. That distinction is central to evaluating enterprise software in the current environment.

Where Risk Is Real

Not all software categories have equivalent structural protection. Risk is most evident in narrowly defined vertical tools, lightweight workflow utilities, and productivity-layer applications with limited proprietary data accumulation. In these segments, generative models can replicate core functionality with relatively low switching friction. Pricing pressure can intensify quickly, and margin compression may prove structural rather than cyclical.

By contrast, enterprise workflow orchestration platforms deeply embedded in core business processes create operational dependency. Replacing them requires redesigning process architecture, not simply substituting interfaces. Systems that accumulate years of transaction data, customization layers, and ecosystem integrations generate switching costs that extend beyond feature parity. Observability and monitoring platforms that collect continuous telemetry function as operational infrastructure; as AI agents proliferate, the need for measurement, traceability, and governance increases rather than declines.

In supply chain software specifically, planning platforms and transportation orchestration systems accumulate integration density over time. That density represents economic friction against displacement and reinforces durability when market volatility increases.

AI as Architectural Pressure

AI will alter software economics. It will increase development intensity, shorten product cycles, and compress margins in commoditized segments. Vendors operating at the surface layer of functionality will face sustained pressure.

However, AI simultaneously increases coordination complexity. As autonomous agents proliferate, enterprises require more governance controls, more integration layers, and more persistent contextual memory. The economic question shifts from “Who can build features fastest?” to “Who can coordinate distributed intelligence most reliably?”

Agent-to-agent communication, contextual memory frameworks, retrieval-based reasoning, and graph-aware modeling are becoming foundational design considerations in supply chain environments, as described in ARC’s white paper AI in the Supply Chain: Architecting the Future of Logistics. Vendors capable of governing these interactions at scale may strengthen their structural position. Vendors confined to interface-layer differentiation may see pricing pressure intensify. The outcome is not uniform decline; it is structural differentiation within the sector.

Valuation vs. Structural Impairment

Markets reprice sectors quickly when uncertainty rises. The current adjustment reflects legitimate concerns: slower growth trajectories, reduced retention durability, increased competitive intensity, and rising research and development requirements. These are measurable economic factors.

The open question is whether valuations reflect permanent impairment across enterprise software broadly, or whether the market is failing to distinguish between commoditized applications and structurally embedded coordination platforms.

Some observers argue that AI may ultimately expand the addressable market for enterprise systems rather than compress it. As AI adoption increases, enterprises may require additional orchestration frameworks, governance layers, and system-level controls. In that scenario, platforms with embedded workflows and distribution reach could see increased strategic relevance. The impact will vary materially by category and architectural depth.

In supply chain markets, complexity is not declining. Cross-border regulation is tightening, network volatility remains elevated, and multi-enterprise coordination is becoming more demanding. Economic value accrues to platforms that integrate and govern transactions, not to those that merely present information.

Implications for Enterprise Buyers

For supply chain leaders, the relevant issue is not short-term equity performance but architectural positioning. Does the platform function as a system of record embedded in transaction flows, or as a reporting layer adjacent to them? How deeply is it integrated into compliance processes, procurement logic, and transportation execution? Does it accumulate proprietary operational data that reinforces switching costs over time? Is it evolving toward coordinated AI architectures, or layering assistive tools onto a static foundation?

AI will not eliminate enterprise systems. It will expose those whose economic value rests primarily on surface functionality rather than integration depth.

A Measured Conclusion

The current narrative captures real pressure within segments of the software sector, but it does not fully account for structural differentiation. Certain categories face sustained pricing compression where differentiation is shallow and switching friction is low. Others may strengthen as AI increases coordination demands, governance requirements, and integration complexity.

The decisive factor will not be branding or feature velocity. It will be integration density, data gravity, and the ability to coordinate distributed intelligence across enterprise and partner networks. In supply chain contexts, platforms that govern transactions, maintain contextual continuity, and orchestrate multi-node operations retain structural advantage. Platforms that merely automate isolated tasks face a more uncertain economic trajectory.

That distinction, rather than headline narrative, will determine long-term outcomes.

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Download the Full Architecture Framework

A2A is only one component of a broader intelligent supply chain architecture. For a structured analysis of how A2A integrates with context-aware systems, retrieval frameworks, graph-based reasoning, and data harmonization requirements, download the full white paper:

AI in the Supply Chain: Architecting the Future of Logistics with A2A, MCP, and Graph-Enhanced Reasoning

The paper outlines the architectural model, governance considerations, and practical implementation path for enterprises building connected intelligence across their supply networks.

Download the white paper to explore the complete framework.

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